Point-based formulation consists of the 3-dimensional coordinates (x, y, z) of points in 3D space.
Point representation is easy to understand and manipulate, but suffers from the ambiguity problem, which means that different forms of point clouds can represent a same geometry.
The pioneer work, PointNet [1]}, processes unordered point data with a single symmetric function - max pooling, to aggregate point features for classification and segmentation.
Fan et al. [2]} developed a deep generative model that generates 3D geometry in point-based formulation from single images. In their work, a loss function based on Earth Mover's distance is introduced to tackle the problem of data ambiguity. However, their method is only validated on the reconstruction task of single objects. No work on point generation for scene reconstruction has been found yet.
